1. Field of the Invention
The present invention relates generally to word-spacing correction technology. More particularly, the present invention relates to a system and a method for automatically recognizing and correcting errors in spacing of word inputs in an electronic device with relatively lower computing power.
2. Description of the Related Art
A mobile device, which includes a broad range of devices and may also be referred to as a portable device, a handheld device, a portable communication terminal, etc., is typically a pocket-sized computing device typically having a display screen with touch input and/or a miniature keyboard (often referred to as a keypad). Many different types of mobile devices are being used today as follows: communication devices, mobile computers, handheld game consoles, media recorders, media players/displayers, and personal navigation devices.
Because of space limitations which are based in part on consumer preference, such mobile devices inherently use inherently a smaller sized input unit with a smaller number of keys than a traditional keyboard. Therefore, a mobile device is not user-friendly in that it is typically rather difficult to input letters. Additionally, for a convenient input, a user often disregards spacing words when inputting a sentence. However, the appearance can be considered quite unprofessional in a business setting, where a note sent from a small pocket-sized device could be read by a client or employer using a desktop computer, and not realizing why the appearance of the message is sloppy.
In an attempt to solve the problem regarding spacing, some approaches have been proposed in the art to automatically space words. One particular approach is based on an analytical technique. To space words, this analytical approach uses heuristic information such as longest matching, shortest matching, morpheme analysis rules, and word-spacing error patterns through vocabulary information. The analytical-based approach, however, needs a great variety of linguistic materials for morpheme analysis and is not cost-effective in constructing and managing linguistic materials. Furthermore, this approach has another drawback of a very low accuracy rate for unregistered words.
Another approach is based on a statistical technique. This statistical approach corrects word-spacing errors by learning, from a corpus of words, the probability of spacing or not between adjacent two syllables. This approach automatically obtains syllable information from a primitive corpus of words, so it may reduce construction costs of materials and also may enhance accuracy for unregistered words. This statistical approach, however, needs a large-sized learning data and a great memory space to obtain reliable probability information.
A widely used way to acquire probability information is an n-gram model using, as learning data, n syllables around a target point for word-spacing. Advantageously, this n-gram model approach can obtain more reliable probability at a higher-sized n-gram, but this may unfavorably require much stronger computing power. It is therefore difficult to directly apply an n-gram model to mobile device with lower computing power. Specifically, it is impossible for mobile devices to use probability information of 2-grams or more.